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---
license: cc-by-nc-sa-4.0
language:
- cs
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: genre
dtype: string
- name: topic
dtype: string
- name: scope
dtype: string
- name: location
dtype: string
- name: argumentation
dtype: string
- name: emotions
dtype: string
- name: overall_sentiment
dtype: string
- name: russia
dtype: string
- name: opinion
dtype: string
- name: expert
dtype: string
- name: source
dtype: string
- name: fear-mongering
dtype: string
- name: blaming
dtype: string
- name: labeling
dtype: string
- name: demonization
dtype: string
- name: relativization
dtype: string
- name: fabulation
dtype: string
splits:
- name: train
num_bytes: 24276218
num_examples: 7642
- name: test
num_bytes: 3180888
num_examples: 1000
download_size: 17695259
dataset_size: 27457106
---
# Dataset Card for the benchmark Propaganda Dataset
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://nlp.fi.muni.cz/projects/propaganda/dataset/
- **Paper:** https://dx.doi.org/10.1016/j.eswa.2024.124085
- **Demo:** https://nlp.fi.muni.cz/projects/propaganda/
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
@article{horak_etal2024_recognition,
title = {Recognition of propaganda techniques in newspaper texts: Fusion of content and style analysis},
author = {Aleš Horák and Radoslav Sabol and Ondřej Herman and Vít Baisa},
journal = {Expert Systems with Applications},
pages = {124085},
year = {2024},
issn = {0957-4174},
publisher = {Elsevier},
doi = {https://doi.org/10.1016/j.eswa.2024.124085},
}
**APA:**
Aleš HORÁK, Radoslav SABOL, Ondřej HERMAN and Vít BAISA.
Recognition of Propaganda Techniques in Newspaper Texts: Fusion of Content and Style Analysis.
Expert Systems with Applications. Elsevier, 2024. ISSN 0957-4174. https://dx.doi.org/10.1016/j.eswa.2024.124085. |